Use the summary of the design to examine the key design properties. Most properties of the design will match the selections that you made for the base design.
However, if the design includes folds or blocks, the final design resolution can differ from the resolution of the base design. Folds can increase the resolution of the design. Blocks can decrease the resolution of the design.
The alias structure describes the confounding pattern that occurs in a design. Terms that are confounded are also said to be aliased.
Aliasing, also known as confounding, occurs in fractional factorial designs because the design does not include all of the combinations of factor levels. For example, if factor A is confounded with the 3-way interaction BCD, then the estimated effect for A is the sum of the effect of A and the effect of BCD. You cannot determine whether a significant effect is because of A, because of BCD, or because of a combination of both. When you analyze the design in Minitab, you can include confounded terms in the model. Minitab removes the terms that are listed later in the terms list. However, certain terms are always fit first. For example, if you include blocks in the model, Minitab retains the block terms and removes any terms that are aliased with blocks.
To see how to determine the alias structure, go to All statistics for Create 2-Level Factorial Design (Specify Generators) and click "Defining relation".
A quality engineer plans to conduct a 9-factor experiment. The engineer uses the 1/16th fraction of the design due to resource limitations. The engineer needs all the 2-factor interactions that involve factors A and B to be free from aliasing with other 2-factor interactions. However, the default generators in Minitab alias two-factor interactions involving factors A or B with other 2-factor interactions. Therefore, the engineer specifies different generators by creating a 5-factor design and specifying generators to add 4 more factors.
When you create your design, Minitab stores the design information in the worksheet. Minitab includes columns for standard order (StdOrder), run order (RunOrder), center points (CenterPt), blocks (Blocks), and a column for each factor. For more information, go to How Minitab stores design information in the worksheet.
You can use the worksheet to guide your experiment because it lists the factor settings for each experimental run and, if you randomized the design, the order in which you should perform the runs. If you didn't randomize the design, you can do that with Modify Design. Before you perform the experiment, you should name one or more columns in the worksheet for the response data. After you enter the response data, you can use Analyze Factorial Design to analyze the design.
For example, this worksheet shows a design with 2 factors, Temperature and Time. The first row in the worksheet contains the first experimental run, where temperature is set to 100 and time is set to 5. After this run is performed, the measurement for strength can be entered into the worksheet.
For more information, go to Checklist of pre-experiment activities.